National open university of nigeria introduction to econometrics I eco 355

part in a survey. 
In statistics, a confidence interval (CI) is a type of interval estimate of a population 
parameter. It is an observed interval (i.e., it is calculated from the observations), in 
principle different from sample to sample, that frequently includes the value of an 
unobservable parameter of interest if the experiment is repeated. How frequently the 
observed interval contains the parameter is determined by the confidence level or 
confidence coefficient. More specifically, the meaning of the term "confidence level" is 
that, if CI are constructed across many separate data analyses of replicated (and possibly 
different) experiments, the proportion of such intervals that contain the true value of the 
parameter will match the given confidence level. Whereas two-sided confidence limits 
form a confidence interval, their one-sided counterparts are referred to as lower/upper 
confidence bounds (or limits). The confidence interval contains the parameter values that, 
when tested, should not be rejected with the same sample. Greater levels of variance yield 
larger confidence intervals, and hence less precise estimates of the parameter. Confidence 
intervals of difference parameters not containing 0 imply that there is a statistically 
significant difference between the populations. 
In applied practice, confidence intervals are typically stated at the 95% confidence level. 
However, when presented graphically, confidence intervals can be shown at several 
confidence levels, for example 90%, 95% and 99%. 
Certain factors may affect the confidence interval size including size of sample, level of 
confidence, and population variability. A larger sample size normally will lead to a better 
estimate of the population parameter. 
Confidence intervals were introduced to statistics by Jerzy Neyman in a paper published 
in 1937 
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